In brief

The non-invasive technique achieved an 87 percent sensitivity and 83 percent specificity in distinguishing lung cancer cases from control samples, paving the way for machine learning-enhanced spectroscopic methods for faster, more reliable lung cancer diagnoses.

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Breathing easy with super sensitive diagnostics

6 Dec 2023

A new approach combines surface-enhanced Raman spectroscopy with advanced computer analysis to accurately and rapidly detect lung cancer by analysing fluid around the lungs.

Just as meteorologists analyse atmospheric data patterns to predict future weather conditions, diagnostic technologies pick up distinct motifs in patient biomarkers to detect disease. In the case of lung cancer diagnostics, Raman spectroscopy has emerged as a promising method of analysing subtle changes in the biochemistry of the fluid surrounding the lungs.

Raman spectroscopy uses laser light to interact with a blood or fluid sample, and by analysing the unique shifts in frequency of the scattered light—known as Raman scattering—creates a molecular ‘fingerprint’ that can be analysed using computational methods. However, in practice, traditional methods for diagnosing lung cancer from Raman spectra can be subjective, time-consuming and inaccurate.

Malini Olivo, who leads the Translational Biophotonics Laboratory at A*STAR Skin Research Labs (A*SRL) where she is a Distinguished Principal Scientist, said that label-free surface-enhanced Raman spectroscopy, or SERS, offers unique advantages for catching lung cancer early.

“Unlike traditional methods that rely on labelling with specific markers which has the possibility [of] poor specificity due to non-specific binding of the labels, label-free SERS directly detects molecular vibrations of specific biomolecules, providing a more rapid and sensitive approach,” explained Olivo, adding that the technique is also non-invasive, sparing patients from painful biopsies.

In collaboration with researchers from the National University Hospital, Singapore, Olivo and A*SRL Senior Scientist, Jayakumar Perumal, tested a novel approach of using SERS as a means of improving the sensitivity of conventional Raman spectroscopy for more sensitive and accurate lung cancer diagnoses.

They collected pleural effusion samples from healthy controls and patients with different medical conditions including lung and breast cancer. Over 500 SERS spectra were acquired for each sample and machine learning techniques were used to classify the samples based on their Raman spectra signatures.

In the study, the team reported an impressive 85 percent classification accuracy for distinguishing lung cancer cases using their new approach. The technique also had an 87 percent sensitivity for correctly identifying lung cancer and an 83 percent specificity for classifying healthy controls.

Olivo said that these findings suggest that SERS may become a new gold standard for lung cancer and beyond. “Since different cancers have distinct molecular profiles, the methodology's ability to detect specific Raman spectral patterns in biofluids could potentially enable the development of diagnostic algorithms for classifying different cancer subtypes.”

The team’s current efforts are focused on validating their findings in a larger patient cohort as well as adapting the methodology for infectious disease diagnostic applications.

The A*STAR-affiliated researchers contributing to this research are from A*STAR Skin Research Labs (A*SRL).

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Perumal, J., Lee, P., Dev, K., Lim, H.Q., Dinish, U.S., et al. Machine learning assisted real-time label-free SERS diagnoses of malignant pleural effusion due to lung cancer. Biosensors 12 (11), 940 (2022). | article

About the Researchers

Jayakumar Perumal is a Senior Scientist I at the Translational Biophotonics Laboratory at A*STAR Skin Research Labs (A*SRL). In 2010, Perumal obtained his PhD in South Korea, specialising in materials engineering and surface chemistry—in particular on polymer-based microfluidics fabrication for various bio-chemical applications. He came to Singapore in 2011 and has been working in A*STAR for more than 12 years. His research interests were mainly on optical diagnostic platform assay development similar to his work on rapid Raman/SERS based portable diagnostics and applying it on different disease biomarker detection. He is working on newer types of non-lithography based scalable plasmonic substrates for point-of-care detection of different bio-chemical analytes. Perumal has several patents and publications to his credit, and he is working towards the development of alpha prototype for early ovarian cancer diagnostics that has drawn strong interest from industries.
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Malini Olivo

Distinguished Principal Scientist

A*STAR Skin Research Labs (A*SRL)
Malini Olivo is a Distinguished Principal Scientist at A*STAR Skin Research Labs (A*SRL) where she leads the Translational Biophotonics Laboratory. Concurrently, she is also an Adjunct Professor at the Lee Kong Chian School of Medicine, NTU; Department of Obstetrics & Gynaecology, National University Health System, NUS, Singapore; and Royal College of Surgeons Ireland, Dublin, Ireland. She obtained a PhD degree in Bio-Medical Physics in 1990 from University Malaya/University College London (UCL) and did her post-doctoral training between 1991 and 1995 at UCL, UK and both McMaster University and University of Toronto, Canada. Her current research interest is in medtech and nano-biophotonics and its applications in translational medicine. Her efforts include bridging the gap between cutting edge optical technologies and unmet clinical needs by developing in-house photonics-based devices for various industries. She has succeeded in obtaining competitive research funding of over USD 25 million to support her research in Singapore and overseas. She has published over 500 papers, three books and 20 book chapters, and filed close to 50 patents on technology platforms and devices. She is also the co- founder of three medtech companies. Furthermore, she holds many advisory international roles and is well recognised internationally for her research in biophotonics for her pioneering research contributions. She has conferred as the Fellow of Optical Society of America (OSA), Fellow of American Institute of Medical Bioengineering (AIMBE) and Fellow of Institute of Physics, UK.

This article was made for A*STAR Research by Wildtype Media Group